國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Approximation of Euclidean metric by...

Mukhopadhyay, Jayanta.

Approximation of Euclidean metric by digital distances

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Approximation of Euclidean metric by digital distancesby Jayanta Mukhopadhyay.
作者:	Mukhopadhyay, Jayanta.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xx, 144 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Distance geometry.
電子資源:	https://doi.org/10.1007/978-981-15-9901-9
ISBN:	9789811599019$q(electronic bk.)

Approximation of Euclidean metric by digital distances
Mukhopadhyay, Jayanta.

Approximation of Euclidean metric by digital distances[electronic resource] /by Jayanta Mukhopadhyay. - Singapore :Springer Singapore :2020. - xx, 144 p. :ill. (some col.), digital ;24 cm.

Geometry, Space and Metrics -- Digital distances: Classes and hierarchies -- Error analysis analytical approaches -- Linear combination of digital distances.

This book discusses different types of distance functions defined in an n-D integral space for their usefulness in approximating the Euclidean metric. It discusses the properties of these distance functions and presents various kinds of error analysis in approximating Euclidean metrics. It also presents a historical perspective on efforts and motivation for approximating Euclidean metrics by digital distances from the mid-sixties of the previous century. The book also contains an in-depth presentation of recent progress, and new research problems in this area.

ISBN: 9789811599019$q(electronic bk.)

Standard No.: 10.1007/978-981-15-9901-9doiSubjects--Topical Terms:

663518
Distance geometry.

LC Class. No.: QA613

Dewey Class. No.: 514.3

Approximation of Euclidean metric by digital distances
LDR:01730nmm a2200337 a 4500 001 591359
003 DE-He213
005 20201202120754.0
006 m d
007 cr nn 008maaau
008 210629s2020 si s 0 eng d
020 $a 9789811599019$q(electronic bk.)
020 $a 9789811599002$q(paper)
024 7 $a 10.1007/978-981-15-9901-9 $2 doi
035 $a 978-981-15-9901-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA613
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 514.3 $2 23
090 $a QA613 $b .M953 2020
100 1 $a Mukhopadhyay, Jayanta. $3 679084
245 1 0 $a Approximation of Euclidean metric by digital distances $h [electronic resource] / $c by Jayanta Mukhopadhyay.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xx, 144 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Geometry, Space and Metrics -- Digital distances: Classes and hierarchies -- Error analysis analytical approaches -- Linear combination of digital distances.
520 $a This book discusses different types of distance functions defined in an n-D integral space for their usefulness in approximating the Euclidean metric. It discusses the properties of these distance functions and presents various kinds of error analysis in approximating Euclidean metrics. It also presents a historical perspective on efforts and motivation for approximating Euclidean metrics by digital distances from the mid-sixties of the previous century. The book also contains an in-depth presentation of recent progress, and new research problems in this area.
650 0 $a Distance geometry. $3 663518
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Functional Analysis. $3 274845
650 2 4 $a Discrete Mathematics in Computer Science. $3 274791
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-15-9901-9
950 $a Computer Science (SpringerNature-11645)